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    "import sys\n",
    "import os\n",
    "if not any(path.endswith('textbook') for path in sys.path):\n",
    "    sys.path.append(os.path.abspath('../../..'))\n",
    "from textbook_utils import *"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
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   "source": [
    "dogs = pd.read_csv('data/akc.csv')\n",
    "\n",
    "kids = {1:\"high\", 2:\"medium\", 3:\"low\"}\n",
    "dogs[\"kids\"] = dogs['children'].map(kids)"
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "(sec:eda_relationships)=\n",
    "# What to Look For in a Relationship"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "When we investigate multiple variables, we examine the relationships between\n",
    "them, in addition to their distributions. In this section, we\n",
    "consider pairs of features and describe what to look for.\n",
    "{numref}`Table %s <feature-plot>` provides guidelines for the type of plot to make\n",
    "based on the feature types. \n",
    "For two features, the combination of types (both quantitative, both qualitative, or a mix) matters.\n",
    "We consider each combination in turn."
   ]
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  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "## Two Quantitative Features\n",
    "\n",
    "If both features are quantitative, then we often\n",
    "examine their relationship with a scatter plot.\n",
    "Each point in a scatter plot\n",
    "marks the position of a pair of values for an observation.\n",
    "So we can think of a scatter plot as a two-dimensional rug plot."
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  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "With scatter plots, we look for linear and simple nonlinear relationships, and we examine the strength of the relationships.\n",
    "We also look to see if a transformation of one or the other or both features leads to a linear relationship."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "The following scatter plot displays the weight and height of dog breeds (both are\n",
    "quantitative):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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    "tags": []
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   "source": [
    "px.scatter(dogs, x='height', y='weight', \n",
    "           marginal_x=\"rug\", marginal_y=\"rug\",\n",
    "           labels={'height':'Height (cm)', 'weight':'Weight (kg)'},\n",
    "           width=350, height=250)"
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    "We observe that dogs that are above average in height tend to be\n",
    "above average in weight. This relationship appears nonlinear: the change in weight for taller dogs grows faster than for shorter dogs. Indeed, that makes sense if we think of a dog as basically shaped like a box: for similarly proportioned boxes, the weight of the contents of the box has a cubic relationship to its length."
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    "It's important to note that two univariate plots are missing information found in a bivariate plot---information about how the two features vary together. \n",
    "Practically, histograms for two quantitative features do not contain enough information to create a scatter plot of the features. We must exercise caution and not read too much into a pair of univariate plots. Instead, we need to use one of the plots listed in the appropriate row of\n",
    "{numref}`Table %s <feature-plot>`\n",
    "(scatter plot, smooth curve, contour plot, heat map,\n",
    "quantile-quantile plot) to get a sense of\n",
    "the relationship between two quantitative features.\n",
    "\n",
    "When one feature is numeric and the other qualitative, {numref}`Table %s <feature-plot>` makes different recommendations. We describe them next."
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    "## One Qualitative and One Quantitative Variable\n",
    "\n",
    "To examine the relationship between a quantitative and a qualitative\n",
    "feature, we often use the qualitative feature to divide the data into groups\n",
    "and compare the distribution of the quantitative feature across these groups.\n",
    "For example, we can compare the distribution of height for small, medium,\n",
    "and large dog breeds with three overlaid density curves:"
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  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": [
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    "fig = plt.figure(figsize=(6, 3))\n",
    "ax = sns.kdeplot(data=dogs, x='height', hue='size')\n",
    "ax.set(xlabel='Height (cm)', ylabel='')\n",
    "ax.get_legend().set_title(\"Size\")\n",
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    "We see that the distribution of height for the\n",
    "small and medium breeds both appear bimodal, with the left mode the larger\n",
    "in each group. Also, the small and medium groups have a larger spread in\n",
    "height than the large group of breeds."
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    "Side-by-side box plots offer a similar comparison of distributions across\n",
    "groups. The box plot offers a simpler approach that can give a crude understanding of a distribution. Likewise, violin\n",
    "plots sketch density curves along an axis for each group. The curve is flipped to create a symmetric \"violin\" shape. The violin plot aims\n",
    "to bridge the gap between the density curve and box plot. We create box plots (left) and violin plots (right) for the height of breeds given the size labeling:"
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  {
   "cell_type": "code",
   "execution_count": 9,
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    "fig = make_subplots(rows=1, cols=2)\n",
    "\n",
    "fig.add_trace(go.Box(x=dogs[\"size\"], y=dogs[\"height\"]), row=1, col=1)\n",
    "fig.add_trace(go.Violin(x=dogs[\"size\"], y=dogs[\"height\"]), row=1, col=2)\n",
    "\n",
    "fig.update_yaxes(range=[0, 90])\n",
    "fig.update_yaxes(title=\"Height (cm)\", row=1, col=1)\n",
    "fig.update_xaxes(\n",
    "    categoryarray=[\"small\", \"medium\", \"large\"], categoryorder=\"array\", \n",
    "    title = \"Size\"\n",
    ")\n",
    "fig.update_layout(showlegend=False, width=550, height=250)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The three box plots of height, one for each size of dog, make it clear that the size categorization is based on\n",
    "height because there is almost no overlap in height ranges for the groups. (This was\n",
    "not evident in the density curves due to the smoothing.) What we don't see in\n",
    "these box plots is the bimodality in the small and medium groups, but we can\n",
    "still see that the large dogs have a narrower spread compared to\n",
    "the other two groups."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Box plots (also known as box-and-whisker plots) give a visual summary\n",
    "of a few important statistics of a distribution.\n",
    "The box denotes the 25th percentile, median, and 75th percentile, the whiskers show the tails, and unusually large or small values are also plotted. \n",
    "Box plots cannot reveal as much shape as a histogram or density curve.\n",
    "They primarily show symmetry and skew, long/short tails, and unusually\n",
    "large/small values (also known as _outliers_).\n",
    "\n",
    "{numref}`Figure %s <box_plot>` is a visual explanation of the parts of a box\n",
    "plot. Asymmetry is evident from the median not being in the middle of the box, the sizes of the tails are shown by the length of the whiskers, and outliers are shown by the points that appear beyond the whiskers. The maximum is considered an outlier because it appears beyond the whisker on the right."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{figure} figures/box_plot.svg\n",
    "---\n",
    "name: box_plot\n",
    "width: 450px\n",
    "---\n",
    "\n",
    "Diagram of a box plot with the summary statistics labeled\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we examine the relationship between two qualitative features, our focus is on proportions, as we explain next."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Two Qualitative Features\n",
    "\n",
    "With two qualitative features, we often compare the distribution of one feature\n",
    "across subgroups defined by the other feature. In effect, we hold one feature constant\n",
    "and plot the distribution of the other one. To do this, we can use some of the same plots\n",
    "we used to display the distribution of one qualitative feature, such as a line plot or \n",
    "bar plot.\n",
    "As an example, let's examine the relationship between the suitability of a breed for children and the size of the breed. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To examine the relationship between these two qualitative features, we calculate three sets of\n",
    "proportions (one each for low, medium, and high suitability). \n",
    "Within each suitability category, we find  the proportion of small, medium, and large dogs. \n",
    "These proportions are displayed in the following table. Notice that each column sums to 1 (equivalent to 100\\%):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": [
     "hide-input"
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    "def proportions(series):\n",
    "    return series / sum(series)\n",
    "\n",
    "counts = (dogs.groupby(['kids', 'size'])\n",
    " .size()\n",
    " .rename('count')\n",
    ")\n",
    "\n",
    "prop_table = (counts\n",
    " .unstack(level=1)\n",
    " .reindex(['high', 'medium', 'low'])\n",
    " .apply(proportions, axis=1)\n",
    ")\n",
    "\n",
    "prop_table_t= prop_table.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
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       "kids    high  medium  low\n",
       "size                     \n",
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       "medium  0.36    0.34  0.2\n",
       "small   0.27    0.37  0.7"
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    "The line plot that follows provides a visualization of these proportions.\n",
    "There is one \"line\" (set of connected dots) for each suitability level.\n",
    "The connected dots give the breakdown of size within a suitability category.\n",
    "We see that breeds with low suitability for kids are primarily small:"
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   ],
   "source": [
    "fig = px.line(prop_table_t, y=prop_table_t.columns, \n",
    "        x=prop_table_t.index, line_dash='kids',\n",
    "        markers=True, width=500, height=250)\n",
    "\n",
    "fig.update_layout(\n",
    "    yaxis_title=\"proportion\", xaxis_title=\"Size\",\n",
    "    legend_title=\"Suitability <br>for children\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also present these proportions as a collection of side-by-side bar plots as shown here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.bar(prop_table_t, y=prop_table_t.columns, x=prop_table_t.index,\n",
    "        barmode='group', width=500, height=250)\n",
    "\n",
    "fig.update_layout(\n",
    "    yaxis_title=\"proportion\", xaxis_title=\"Size\", \n",
    "    legend_title=\"Suitability <br>for children\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far, we've covered visualizations that incorporate one or two features.\n",
    "In the next section, we discuss visualizations that incorporate more than two features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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